Data fusion between aerial photos and LiDAR provides better estimates in forestry and ecological applications, because LiDAR mainly provides the structural information of objects and aerial photo can add spectral information to them. Without the data fusion, an accurate identification of tree crown information from two dimensional data is difficult due to shaded and shadow pixels cast on the image and image distortion. The aerial photogrammetric techniques cannot reconstruct the objects accurately in three dimensional spaces if they are not clearly visible on the photos. The conventional orthophotos, therefore, still have image distortion due to an inappropriate Digital Surface Model (DSM). LiDAR provides a more suitable surface of tree crown structure in three-imensional spaces. This LiDAR-derived DSM could be used in conjunction with conventional photogrammetric techniques to rectify aerial photos and produce true orthophotos for each image. The existence of different perspective points from the use of multiple images results in different illumination and shadows cast on the DSM from the angle between the sun and the camera. In previous studies, a Z-buffer algorithm was applied for the occlusion detection and compensation. However, the technique was computationally intensive. In this study, the camera view and sun-oriented hillshade were generated using the LiDAR-derived DSM. The hillshade surfaces distinguished between the exposed and the occluded side of the DSM during the composition process of respective true orthophotos. This technique constituted a simpler approach and is applicable to data fusion between LiDAR and multispectral imagery to make an orthographically rectified image.

Stand delineation is an important step in the process of establishing a forest inventory and provides the spatial framework for many forest management decisions. Many methods for extracting forest structure characteristics for stand delineation and other purposes have been researched in the past, primarily focusing on high-resolution imagery and satellite data. High-resolution airborne laser scanning offers new opportunities for evaluating forests and conducting forest inventory. This study investigates the use of information derived from light detection and ranging (LIDAR) data as a potential tool for delineation of forest structure to create stand maps. Delineation methods are developed and tested using data sets collected over the Blue Ridge study site near Olympia, Washington. The methodology developed delineates forest areas using LIDAR data and object-oriented image segmentation and supervised classification. Error matrices indicate classification accuracies with a kappa hat values of 78 and 84% for 1999 and 2003 data sets, respectively.

Forest structure data derived from lidar is being used in forest science and management for inventory analysis, biomass estimation, and wildlife habitat analysis. Regression analysis dominated previous approaches to the derivation of tree stem and crown parameters from lidar. The regression model for tree parameters is locally applied based on vertical lidar point density, the tree species involved, and stand structure in the specific research area. The results of this approach, therefore, are location-specific, limiting its applicability to other areas. For a more widely applicable approach to derive tree parameters, we developed an innovative method called ‘wrapped surface reconstruction’ that employs radial basis functions and an isosurface. Utilizing computer graphics, we capture the exact shape of an irregular tree crown of various tree species based on the lidar point cloud and visualize their exact crown formation in three-dimensional space. To validate the tree parameters given by our wrapped surface approach, survey-grade equipment (a total station) was used to measure the crown shape. Four vantage points were established for each of 55 trees to capture whole-tree crown profiles georeferenced with post-processed differential GPS points. The observed tree profiles were linearly interpolated to estimate crown volume. These fieldwork-generated profiles were compared with the wrapped surface to assess goodness of fit. For coniferous trees, the following tree crown parameters derived by the wrapped surface method were highly correlated (p<0.05) with the total station-derived measurements: tree height (R2=0.95), crown width (R2=0.80), live crown base (R2=0.92), height of the lowest branch (R2=0.72), and crown volume (R2=0.84). For deciduous trees, wrapped surface-derived parameters of tree height (R2=0.96), crown width (R2=0.75), live crown base (R2=0.53), height of the lowest branch (R2=0.51), and crown volume (R2=0.89) were correlated with the total station-derived measurements. The wrapped surface technique is less susceptible to errors in estimation of tree parameters because of exact interpolation using the radial basis functions. The effect of diminished energy return causes the low correlation for lowest branches in deciduous trees (R2=0.51), even though leaf-off lidar data was used. The wrapped surface provides fast and automated detection of micro-scale tree parameters for specific applications in areas such as tree physiology, fire modeling, and forest inventory.

High density airborne LiDAR data has been used to provide detailed information on tree canopy structure. Recent research has reported that it is difficult to determine where the LiDAR data errors appear on the tree crown, as the returns generally underestimate tree height. This study introduces a novel way to assess vertical and horizontal patterns of airborne LiDAR data error using an error mapping technique. We use two field sampling techniques to obtain accurate measurements of tree crowns, including a surveying total station (Nikon Inc., USA) and the ground-based LIDAR system (Leica HDS 3,000, Leica Geosystems, USA). These field observations are compared with airborne LiDAR data to clarify the capability of airborne LIDAR sensors. We use an isolated pine tree in the Washington State Park Arboretum. To quantify the error between field observation and airborne LiDAR data, we first generate a numerical 'wrapped' surface around a cloud of discrete LIDAR points. Then the errors of two different field assessment approaches are mapped and are quantified relative to the wrapped surface of airborne LiDAR systems. Field validation using the total station had a mean error of 6.8 cm. The mean error from a ground based LiDAR was 22.9 cm. Both field tools produce an overestimation of crown volume relative to the crown volume produced from airborne LiDAR data. This error mapping technique is also used to detect tree growth using LiDAR data from different years, which is useful information for managers and tree physiologists.

Tree canopy structure is an important factor in forest fire, plant physiology, and tree competition. Quantifying the tree canopy structures is difficult due to the irregular shapes and spacing of the trees. Our method for estimating the canopy structure is based on Light Detection and Ranging (LIDAR) data. LIDAR has three-dimensional point distribution which allows us to ascertain the shape of objects on the ground. Our method consists of three steps. First, we partition the LIDAR points into subsets corresponding to individual trees using level set methods. Second, for each tree we select a subset of points near the crown surface. Finally, we use an isosurface method with radial basis functions to reconstruct the crown surface of each tree from the selected points. The resulting surface provides more precise information about crown base height, which was difficult to measure from discrete points in previous studies. Our approach improves the spatial accuracy of tree level parameters and provides 3D images of crown shapes.

ABSTRACT Knowledge of tree crown information is critical to the modeling of forest fires. For example, FARSITE, one of the most commonly used fire simulators, uses crown volume to estimate crown fire behavior. Conventionally, tree crown volume is estimated using plot-level regression models from LIDAR (Light Detection and Ranging) data of the canopy structure. Convectional approaches, however, tend to result in rough estimations of crown volume. A more accurate method for computing single stand-level crown volume from LIDAR data would result in improving species characterization, automating tree identification from LIDAR data, and simulating fire behavior more precisely than conventional approaches. In this research, we propose a novel technique which more accurately computes individual tree crown volume from LIDAR data. First, we identify individual trees from unorganized LIDAR data points using a level set method, a shortest path algorithm and known GPS points for the stem locations. Second, we use radial basis functions (RBFs) to reconstruct implicit surfaces approximating individual tree crown shapes. These implicit surfaces, which effectively "wrap" each tree crown, are used to reconstruct a Digital Surface Model (DSM) of the canopy and to estimate individual tree crown volume.

Airborne laser altimetry (Lidar) can produce topographic maps of amazing detail and accuracy, even where the ground is obscured by forest canopy. Detailed Lidar topography can identify possible landing locations, difficult stream crossings, unstable soils, difficult side-slopes, and useful benches. This detail can reduce field time, guide road designs towards better options, and improve confidence in our cost estimates. Lidar mapping can occasionally fail however, and how these failures are represented will determine Lidar’s reliability and value for road design. We discuss first experiences with an operational Lidar mapping of the Tahoma State Forest, south of Mt. Rainier. This detailed topographic mapping was used in forest operations design such as landing and road locations as part of a watershed-based harvest and transportation plan. Lidar-based in-office designs were subsequently field-verified. Critical to the success of such DEM’s for forest engineering design was the ability (or lack thereof) to distinguish between areas of adequate or marginal ground point coverage leading to excellent or erroneous mapping detail. We discuss various methodologies that would identify areas of marginal Lidar ground point coverage leading to a first set of Lidar data collection requirements mapping contractors should adhere to. SEEING UNDER THE CANOPY A recurring problem in timber harvest and road planning is that the trees that intended for harvest can hide the ground over which logs must be yarded and roads must be built. The topographic maps that are commonly used in planning are based on aerial photographs in which the stands that we now want to harvest have obscured the ground over which we must plan. The resulting topography is thus a map of the top canopy, with an offset for the assumed tree height. Unfortunately, the canopy does not follow the ground exactly, and the minor topographic variations that can be crucial in harvest and road planning are not reflected in the top of the resulting canopy. The topography often includes areas of soil instability, rock outcrops, and uneven topography that can present difficulties in harvest and roading. The canopy can also obscure natural mounds and benches that can serve as convenient landing and road locations. As a result, these topographic maps can only serve as a general guide for design, and critical elements of the operation will need to be based on field verification. Recent developments in airborne laser topographic scanning (Lidar) allow for detailed topographic mapping even under forest canopy. Lidar works by shooting millions of

Stream sedimentation concerns might probably be incorporated into the road design process as a simplified, intuitive thumbnail model. Road Density (RD) and Stream Crossing Density (SCD) however guide designers towards minimizing minor, unused spur roads that have minimal sediment impacts. Crossing Area Served (CAS) on the other hand, focuses design attention towards the most heavily traveled segments, tending to shift them away from the stream network and towards a ridge alignment. A case study suggest that CAS better identifies areas of high sediment delivery and the design options that reduce this sediment.

The operational and environmental impacts of a conventional and a long-span yarding approach to forest management were simulated and compared in T12N R14E in the Ahtanum valley West of Yakima, WA. The conventional approach produced higher revenues at lower costs as expected, but delivered no more sediment to the stream than the long-span, no-new-roads approach. The explanation for this counter-intuitive result can be found in the density of the road network and its proximity to the stream. The road network produced a tenfold sediment increase over background levels, which might suggest a program of elimination and/or surfacing of existing roads. Analysis of this case, however, suggests that the construction of a ridge-based road network will be both environmentally and economically superior. This approach of integrating cumulative environmental impacts into the landscape scale harvest and transportation planning appears promising for identifying management options for reducing salmonid habitat degradation.

Currently, forest management and environmental protection (even under adaptive management) are commonly planned at the scale of a single operation. A harvest is scheduled, a road is built, and regulations are applied without detailed consideration of how it will impact neighboring operations, and the cumulative present and future environmental impacts they produce. These approaches may obtain a locally optimal solution, but combining many locally optimal solutions across a landscape generally produces a solution that is not optimal for the system overall. For example, a collection of locally optimal harvest units may leave patches between them that are poorly accessed by any of the locally optimal harvest units. As another example, prohibiting yarding across a stream will prevent a one time disruption to the riparian buffer, but may require additional road segments (and impacts that they entail) to access the far side of the stream. Finding economically and environmentally optimal solutions for a landscape requires planning the forest management at the landscape scale. Any number of such plans are possible, with differing environmental objectives, road networks, and silvicultural options. For example, one might want to reduce road density by shifting to larger harvest units and longer yarding distances. Some sort of tool is needed to compare the economic and environmental impact of alternate plans and options. The economic impacts of management activity can be accumulated through net present value of each action. Road construction, maintenance and decommissioning as well as silvicultural operations (planting, thinning, harvest, site preparation, etc.) all have costs and/or yields whose net present value can be estimated. The sum net present value of all activities in a plan can then be estimated and accumulated, and compared with the net present value of alternate plans to identify the economically superior option. A similar metric is needed to identify the environmental costs of alternate management plans by accumulating environmental costs of each action, at each point in the landscape, over the period of the plan. Such an environmental equivalent of net present value might be called ‘cumulative impact’. The basic hydrological component models of such a framework already exist in one form or another. Basic attempts have

The spray-distribution pattern from a prototype sludge-application vehicle was not differentially affected by varying stand conditions during spray tests in stands of Douglas-fir (Pseudotsuga menziesii (Mirbel) Franco). The test-stand stockings ranged from 345 to 885 trees ha−1. An optimal trail spacing of 65 m was established for the prototype vehicle. The spacing was determined by the throw distance from the vehicle and the amount of spray overlap required from adjacent trails to produce a uniform application. The tests indicated that a uniform sludge application can be achieved in a forest environment with approximately 6% of the application area consisting of access trails.

A model has been developed which allows a designer to optimize the location of ditch relief culverts to minimize sediment deliveries to streams. The model provides immediate visual feedback allowing the designer to rapidly evaluate and optimize various ditch relief culvert locations. The model's sediment delivery and routing algorithms are based on existing methodologies. Current as well as planned road systems can be evaluated and the potential for improvements documented in a quantifiable and repeatable way. The model was tested on a portion of the Tahoma State Forest, situated south of Mt. Rainier. Two existing road systems with 28 and 39 stream crossings and 82 and 86 ditch relief culverts respectively were analyzed. Interactively relocating 20 and 35 ditch relief culverts resulted in a 76% reduction in sediments delivered to each stream system. The last culvert was usually placed 100 to 200 feet from a stream. CROSS DRAIN LOCATION, SEDIMENT DELIVERY AND TOPOGRAPHY Cross drain systems were originally devised for reducing the adverse effects of excess water within the roadway. The recent evolution of road design into a more environmentally aware paradigm lead to a new challenge for the cross drain systems design. They emerged as a potential solution to the stream sedimentation problem by intercepting and rerouting sediment- laden ditch water. Thus, in addition to the functionality dictated by the prism health state, another prerequisite was added: to reduce sediment delivery to stream networks (FAO, 1989; Washington State Forest Practices Board, 2000).

Tree location and parameters are considered fundamental information in designing logging operations. A small footprint Light Detection and Ranging (LIDAR) can provide microscale information for individual tree parameters because of high point density. Conventionally, tree parameters given by LIDAR data are estimated at the scale of inventory plots or circular sampling plots. The findings are then averaged to the whole units. However, LIDAR data can provide more microscale tree parameters such as individual tree height and tree crown diameter. In this research, we introduce an efficient method to obtain individual tree tops from a group of LIDAR points in a large area and identify tree location, which yields important information for setting skyline cableways. To achieve this, tree tops are found by the local maxima of stationary points on Digital Surface Models (DSMs). The tree tops derived from LIDAR are verified with stem locations collected in the field and displayed with Digital Terrain Models (DTMs) to show the location of trees sufficiently large to be used for skyline operation.

By evaluating alternative routes in the office using a pegging routine, days or even weeks can be saved of valuable field time and ultimately, a better design can emerge. Initial road design in forested landscapes often includes pegging roads on large-scale contour maps with dividers and an engineers scale. An automated GIS based road-pegging tool (PEGGER) was developed to assist in initial road planning by automating the road pegging process. PEGGER is an extension for the commonly available GIS software Arcview®. PEGGER imports topography as digital contours. The user identifies the origin of the new road, clicks in the direction they want to go and PEGGER automatically pegs in road at a specified grade. Through the use of PEGGER, many alternatives can be quickly analyzed for alignment, slope stability, grades and construction cost using standard GIS functionality. The resulting cuts and fills are then displayed in ROADVIEW, a road visualization package for Arcview®. This paper looks at the algorithm used, evaluates it's usefulness in an operations planning environment and suggests additional methods which might be incorporated into PEGGER to further assist the forest engineer.

By evaluating alternative routes in the office using a pegging routine, days or even weeks can be saved of valuable field time and ultimately, a better design can emerge. Initial road design in forested landscapes often includes pegging roads on large-scale contour maps with dividers and an engineers scale. An automated GIS based road-pegging tool (PEGGER) was developed to assist in initial road planning by automating the road pegging process. PEGGER is an extension for the commonly available GIS software Arcview®. PEGGER imports topography as digital contours. The user identifies the origin of the new road, clicks in the direction they want to go and PEGGER automatically pegs in road at a specified grade. Through the use of PEGGER, many alternatives can be quickly analyzed for alignment, slope stability, grades and construction cost using standard GIS functionality. The resulting cuts and fills are then displayed in ROADVIEW, a road visualization package for Arcview®. This paper looks at the algorithm used, evaluates it's usefulness in an operations planning environment and suggests additional methods which might be incorporated into PEGGER to further assist the forest engineer.